{"id":"W2398981024","doi":"","title":"SINAI at RTE-7: Integrating Personalized Page Rank Vectors into EDITS.","year":2011,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rank (graph theory); Computer science; Textual entailment; Similarity (geometry); Information retrieval; Natural language processing; Logical consequence; Artificial intelligence; Term (time); Value (mathematics); Machine learning; Mathematics; Combinatorics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00314609,0.001122641,0.0009991779,0.00263257,0.0005752652,0.002218591,0.00114808,0.001292528,0.009312669],"category_scores_gemma":[0.01739321,0.0005116552,0.0009140635,0.001754287,0.0003906229,0.004221281,0.001514594,0.00145179,0.006719628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000590586,"about_ca_system_score_gemma":0.0009071874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003258208,"about_ca_topic_score_gemma":0.00835828,"domain_scores_codex":[0.9974012,0.0008573955,0.0001689993,0.0006220425,0.0008349328,0.0001154631],"domain_scores_gemma":[0.9953427,0.001752788,0.0003623292,0.001346848,0.0009576745,0.0002375936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007410313,0.0004627646,0.004275496,0.000468186,0.0001740367,0.0001890027,0.0002862919,0.01418376,0.01168474,0.01323214,0.07344421,0.8808584],"study_design_scores_gemma":[0.0001201723,0.0007127928,0.007109988,0.00009299124,0.0001164008,0.0007722689,0.0002677985,0.7847987,0.03820387,0.04839708,0.1192705,0.0001374599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04317116,0.002167503,0.8687486,0.001168757,0.0007027085,0.0007815285,0.01388998,0.05844817,0.01092159],"genre_scores_gemma":[0.1401517,0.0007439673,0.8014347,0.0002939675,0.000338423,0.0004151967,0.03311862,0.002218712,0.02128469],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009312669,"threshold_uncertainty_score":0.03115398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01040798563329431,"score_gpt":0.2532511661136341,"score_spread":0.2428431804803398,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}